Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Urdu
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use kingabzpro/wav2vec2-60-urdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kingabzpro/wav2vec2-60-urdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-60-urdu")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kingabzpro/wav2vec2-60-urdu") model = AutoModelForCTC.from_pretrained("kingabzpro/wav2vec2-60-urdu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from kingabzpro/wav2vec2-60-urdu: direct link, hf CLI and curl.
- Browser
- Download file 214 Bytes
-
https://huggingface.co/kingabzpro/wav2vec2-60-urdu/resolve/66ae7886ff828203c2a0dd6751bb076eb4a09599/preprocessor_config.json
- Command line
-
hf download hf://kingabzpro/wav2vec2-60-urdu@66ae7886ff828203c2a0dd6751bb076eb4a09599/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/kingabzpro/wav2vec2-60-urdu/resolve/66ae7886ff828203c2a0dd6751bb076eb4a09599/preprocessor_config.json
214 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |